Video Classification Usinig Semi-supervised Learning Methods

Please enable javascript in your browser.

This page was sent successfuly

Bookmark

706 viewed

دسته بندی داده های ویدئویی با استفاده از روش های یادگیری نیمه نظارتی

706 Viewed

Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 42974 (19)
University: Sharif University of Technology
Department: Computer Engineering
Advisor(s): Kasaei, Shohreh
Abstract:
In large databases, availability of labeled training data is mostly prohibitive in classification. Semi-supervised algorithms are employed to tackle the lack of labeled training data problem. Video databases are the epitome for such a scenario; that is why semi-supervised learning has found its niche in it. Graph-based methods are a promising platform for semi-supervised video classification. Based on the multiview characteristic of video data, different features have been proposed (such as SIFT, STIP and MFCC) which can be utilized to build a graph. In this project, we have proposed a new classification method which fuses the results of manifold regularization over different graphs. Our method acts like a co-training method with respect to its iterative nature which tries to find the labels of unlabeled data during each iteration, but unlike co-training methods it takes into account the unlabeled data in classification procedure. The fusion is done after manifold regularization with a ranking method which makes the algorithm to be competitive with supervised methods. Our experimental results run on the CCV database shows the efficiency of the proposed method.
Keywords:
Semi-Supervised Learning ; Co-Training ; Video Data Classification

No TOC